Results 151 to 160 of about 219,349 (181)

Imbalanced Extreme Learning Machine for Classification with Imbalanced Data Distributions

2016
Due to its much faster speed and better generalization performance, extreme learning machine (ELM) has attracted many attentions as an effective learning approach. However, ELM rarely involves strategies for imbalanced data distributions which may exist in many fields.
Wendong Xiao, Jie Zhang, Yanjiao Li, Weidong Yang   +3 more
openaire   +1 more source

Hybrid sampling for imbalanced data

2008 IEEE International Conference on Information Reuse and Integration, 2008
Building a classification model on imbalanced datasets can be a challenging endeavor. Models built on data where examples of one class are greatly outnumbered by examples of the other class(es) tend to sacrifice accuracy with respect to the underrepresented class in favor of maximizing the overall classification rate.
Seiffert, Chris, Khoshgoftaar, Taghi M., Van Hulse, Jason   +2 more
openaire   +1 more source

Online learning for imbalanced data

2018
<p>Online learning receives increasing attention due to its efficiency in handling large-scale streaming data. However, imbalanced data raises a big challenge for traditional online algorithms which aim at minimizing the misclassification error rate.
Xiaoxuan Zhang, Tianbao Yang, Padmini Srinivasan, Nick Street, Kasturi Varadarajan, Xun Zhou   +5 more
openaire   +1 more source

Learning from Imbalanced Data Streams

2018
Mining data streams is one of the most vital fields in the contemporary ML. Increasing number of real-world problems are characterized by both volume and velocity of data, as well as by evolving characteristics. Learning from data stream assumes that new instances arrive continuously and that their properties may change over time due to a phenomenon ...
Alberto Fernández, Salvador García, Mikel Galar, Ronaldo C. Prati, Bartosz Krawczyk, Francisco Herrera   +5 more
openaire   +1 more source

Dealing with Imbalanced Data

2022
Neelam Rout, Debahuti Mishra, Manas Kumar Mallick, Pradeep Kumar Mallick   +3 more
openaire   +1 more source

Boosting classifications with imbalanced data

2017
Boosting is an ensemble method which uses a weak classifier to create a strong one, based on the theory of Robert Schapire s work in 1990 (see Schapire 1990). It appears similar to bagging yet is fundamentally different. This thesis will start with a short introduction followed by a chapter describing the theory and methodology behind ...
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Feature Selection in Imbalanced Data

Annals of Data Science, 2022
Firuz Kamalov, Fadi Thabtah, Ho Hon Leung   +2 more
openaire   +1 more source

Imbalanced Classification for Big Data

2018
New developments in computation have allowed an explosion for both data generation and storage. The high value that is hidden within this large volume of data has attracted more and more researchers to address the topic of Big Data analytics. The main difference between addressing Big Data applications and carrying out traditional DM tasks is ...
Alberto Fernández, Salvador García, Mikel Galar, Ronaldo C. Prati, Bartosz Krawczyk, Francisco Herrera   +5 more
openaire   +1 more source

An overview of real‐world data sources for oncology and considerations for research

Ca-A Cancer Journal for Clinicians, 2022
Lynne Penberthy, Donna R Rivera, Jennifer L Lund, Msph   +2 more
exaly  

Imbalanced Data for Knowledge Tracing

2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan), 2023
Jyun-Yi Chen, I-Wei Lai
openaire   +1 more source